Speaker individuality information is among the most critical elements within speech signals.
By thoroughly and accurately modeling this information, it can be utilized in various …

As the most widely used technique, deep speaker embedding learning has become
predominant in speaker verification task recently. Very large neural networks such as …

With the development of deep learning, automatic speaker verification has made
considerable progress over the past few years. However, to design a lightweight and robust …

Even though deep speaker models have demonstrated impressive accuracy in speaker
verification tasks, this often comes at the expense of increased model size and computation …

Recently, speaker verification systems benefit from deep neural networks and the size of
speaker embedding encoder increases with these sophisticated architectures. Nevertheless …

Very deep models for speaker recognition (SR) have demonstrated remarkable performance
improvement in recent research. However, it is impractical to deploy these models for on …

Modern speaker verification (SV) systems typically demand expensive storage and
computing resources, thereby hindering their deployment on mobile devices. In this paper …

With the continuous development of deep neural networks (DNN) in recent years, the
performance of speaker verification systems has been significantly improved with the …

Reducing noise and non-speech segments that degrade speaker verification (SV)
performance requires voice activity detection (VAD) and speech enhancement (SE) …

Mobile devices are integral to daily life, necessitating secure authentication methods like
speaker verification for enhanced security and convenience. While deep neural networks …